





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__A							   =							{
    "configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A							   =							["ConvNextFeatureExtractor"]
     __A							   =							["ConvNextImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A							   =							[
         "CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "ConvNextForImageClassification",
         "ConvNextModel",
         "ConvNextPreTrainedModel",
         "ConvNextBackbone",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A							   =							[
         "TFConvNextForImageClassification",
         "TFConvNextModel",
         "TFConvNextPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_convnext import ConvNextFeatureExtractor
          from .image_processing_convnext import ConvNextImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnext import (
              CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextBackbone,
              ConvNextForImageClassification,
              ConvNextModel,
              ConvNextPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
     import sys

     __A							   =							_LazyModule(__name__, globals()["__file__"], _import_structure)


def 	lowerCamelCase					(	UpperCamelCase     :				int							)   ->    bool:
		_lowerCamelCase				   =  (1 + 24 * n) ** 0.5
		return ((1 + root) / 6) % 1 == 0


def 	lowerCamelCase					(	UpperCamelCase     :				int = 50_00							)   ->    int:
		_lowerCamelCase				   =  [(i * (3 * i - 1)) // 2 for i in range(1			,       UpperCamelCase							)]
		for i, pentagonal_i in enumerate(UpperCamelCase							):
				for j in range(UpperCamelCase			,       len(UpperCamelCase							)							):
						_lowerCamelCase				   =  pentagonal_nums[j]
						_lowerCamelCase				   =  pentagonal_i + pentagonal_j
						_lowerCamelCase				   =  pentagonal_j - pentagonal_i
						if is_pentagonal(UpperCamelCase							) and is_pentagonal(UpperCamelCase							):
								return b

		return -1


if __name__ == "__main__":
				print(F'''{solution() = }''')